The DOI matches its Crossref record; 241 of the 241 references it cites were found in a registry or an index; none of the 112 sources its claims cite is checked for support yet.
Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues
Open accessAbstract
Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in this system. However, integrating with AI, the system would be multiple agent collaborators who are capable of communicating with their desired host efficiently. Again, FL is another interesting feature, which works decentralized manner; it maintains the communication based on a model in the preferred system without transferring the raw data. The combination of FL, AI, and XAI techniques can be capable of minimizing several limitations and challenges in the healthcare system. This paper presents a complete analysis of FL using AI for smart healthcare applications. Initially, we discuss contemporary concepts of emerging technologies such as FL, AI, XAI, and the healthcare system. We integrate and classify the FL-AI with healthcare technologies in different domains. Further, we address the existing problems, including security, privacy, stability, and reliability in the healthcare field. In addition, we guide the readers to solving strategies of healthcare using FL and AI. Finally, we address extensive research areas as well as future potential prospects regarding FL-based AI research in the healthcare management system.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This survey reviews federated learning (FL), artificial intelligence (AI), and explainable AI (XAI) for smart healthcare, proposing taxonomies and discussing integration benefits, challenges, and future directions. It reports performance metrics from prior studies and identifies open issues in security, privacy, reliability, scalability, and dataset availability.
Key findings
- The paper presents a comprehensive FL-AI-healthcare taxonomy and classifies applications including EHR management, remote health monitoring, biomedical image analytics, and COVID-19 detection.
- It identifies major existing healthcare challenges—security/privacy, reliability, confidentiality, and scalability—and discusses FL, FL+AI, and FL+XAI as solution strategies.
- Table 7 reports accuracy/AUC from reviewed works: e.g., BlockFL on Pima Indians diabetes accuracy 0.84; Naïve Bayes on Abalone 0.98 and Random Forest on Wine 0.99; FL(Lasso) AUC 0.693–0.805 across NYC hospital datasets; FL without noise (MLP) AUC 0.796–0.834; Fed-Align-CL AUC 0.70–0.84; Unet 0.94 accuracy; ST-CNN-GAP-6 0.95 accuracy and 0.99 AUC; VGG16 0.97 accuracy and AUC; Random Forest 0.919 accuracy; Random Forest 87.76 accuracy and 0.953 AUC.
- Future directions include FL in data science, AI with blockchain for security, controlling robots using FL-AI, FL with big data analytics, FL-blockchain in Industry 5.0, and FL-blockchain for accessing medical data.
Methods
- Design: a narrative survey/review that integrates concepts, taxonomies, motivations, challenges, and open issues for FL-based AI in smart healthcare.
- Data or sample: no primary dataset or experiment; it analyzes existing literature and summarizes prior works in Tables 2–8, including datasets used in FL, AI, XAI, and healthcare.
- Main analysis: conceptual and taxonomic synthesis, comparative tables of related works, and a performance-metric analysis using accuracy and AUC values extracted from previously published studies.
Limitations
- Paper-stated: healthcare is not limited to AI and FL techniques, and various problems and challenges still need to be addressed; open issues include suitable dataset scarcity, non-IID data and outliers, expensive and time-consuming training, and unsatisfactory generalization of current ML models in the medical sector.
- Evident from the text: the work is a survey without primary data collection, systematic review protocol, or meta-analysis, so its performance comparisons rely on heterogeneous prior studies.
- Evident from the text: Table 7 mixes different datasets, methods, and tasks, so the reported accuracy/AUC values are not directly comparable across studies.
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Claims the paper makes
81 Not checked yet · 31 Own interpretation · 11 No basis given
In the paper
“FL, for instance, has made e-health services more accessible by allowing machine learning (ML) modeling despite the absence of health data [10].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Introduction
What the sources say
- Not checked yetcites [10] Sheller, M.J., Reina, G.A., Edwards, B., Martin, J., Bakas, S.: Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation. In: International MICCAI Brainlesion Workshop, pp. 92–104. Springer (2018) source
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Health data owners, such as hospitals, can avoid exchanging healthcare information by employing FL.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“More specifically in e-healthcare, personal data are subjected to regulations like the Health Insurance Portability and Accountability Act of the United States (HIPPA) [8].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Introduction
What the sources say
- Not checked yetcites [8] Cheng, V.S., Hung, P.C.: Health insurance portability and accountability act (hippa) compliant access control model for web services. Int. J. Healthcare Informat. Sys. Informatics (IJHISI) 1(1), 22–39 (2006) source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Federated Learning, a new distributed interactive AI concept, is especially promising for smart healthcare since it allows numerous clients (such as Hospitals) to participate on AI training whilst maintaining data privacy.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Introduction
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In the paper
“FL allows training of AI models through averaging of local updates from numerous healthcare facilities and smart devices, such as IoMT, despite the lack of local data.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Introduction
What the sources say
- Not checked yetcites [11] Warnat-Herresthal, S., Schultze, H., Shastry, K.L., Manamohan, S., Mukherjee, S., Garg, V., Sarveswara, R., Händler, K., Pickkers, P., Aziz, N.A., et al.: Swarm learning for decentralized and confidential clinical machine learning. Nature 594(7862), 265–270 (2021) source
- Not checked yetcites [12] Sheller, M.J., Edwards, B., Reina, G.A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R.R., et al.: Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Scientific Reports 10(1), 1–12 (2020) source
- Not checked yetcites [13] Kaissis, G., Ziller, A., Passerat-Palmbach, J., Ryffel, T., Usynin, D., Trask, A., Lima, I., Mancuso, J., Jungmann, F., Steinborn, M.M., et al.: End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nat. Mach. Intell. 3(6), 473–484 (2021) source
- Not checked yetcites [14] Kaissis, G.A., Makowski, M.R., Rückert, D., Braren, R.F.: Secure, privacy-preserving and federated machine learning in medical imaging. Nat. Mach. Intell. 2(6), 305–311 (2020) source
- Not checked yetcites [15] Rahman, A., Islam, M.J., Sunny, F.A., Nasir, M.K.: Distblocksdn: A distributed secure blockchain based sdn-iot architecture with nfv implementation for smart cities. In: 2019 2nd International Conference on Innovation in Engineering and Technology (ICIET), pp. 1–6 (2019). https://doi.org/10.1109/ICIET48527.2019.9290627 source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Federated Learning has presented itself as a viable method for implementing economic, innovative healthcare systems while ensuring privacy [11–15].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Introduction
What the sources say
- Not checked yetcites [11] Warnat-Herresthal, S., Schultze, H., Shastry, K.L., Manamohan, S., Mukherjee, S., Garg, V., Sarveswara, R., Händler, K., Pickkers, P., Aziz, N.A., et al.: Swarm learning for decentralized and confidential clinical machine learning. Nature 594(7862), 265–270 (2021) source
- Not checked yetcites [12] Sheller, M.J., Edwards, B., Reina, G.A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R.R., et al.: Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Scientific Reports 10(1), 1–12 (2020) source
- Not checked yetcites [13] Kaissis, G., Ziller, A., Passerat-Palmbach, J., Ryffel, T., Usynin, D., Trask, A., Lima, I., Mancuso, J., Jungmann, F., Steinborn, M.M., et al.: End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nat. Mach. Intell. 3(6), 473–484 (2021) source
- Not checked yetcites [14] Kaissis, G.A., Makowski, M.R., Rückert, D., Braren, R.F.: Secure, privacy-preserving and federated machine learning in medical imaging. Nat. Mach. Intell. 2(6), 305–311 (2020) source
- Not checked yetcites [15] Rahman, A., Islam, M.J., Sunny, F.A., Nasir, M.K.: Distblocksdn: A distributed secure blockchain based sdn-iot architecture with nfv implementation for smart cities. In: 2019 2nd International Conference on Innovation in Engineering and Technology (ICIET), pp. 1–6 (2019). https://doi.org/10.1109/ICIET48527.2019.9290627 source
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As a result of FL’s distributed learning characteristic, the smart healthcare system’s scalability may be improved.”
- Also stated in: § e-Healthcare system · hedged
“As a result of FL’s distributed learning characteristic, the smart healthcare system’s scalability may be improved.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Also stated in: § e-Healthcare system · hedged
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Because model gradients are typically much smaller than their actual datasets, FL can be deployed to mitigate communication expenses, such as delay and power dispatch associated with communication of local data.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Enhancement of Data Privacy: In FL, local data are not required for training.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“FL offers security features which creates an excellent alternative for designing smart and secured IoT devices, especially in light of increasingly severe data privacy protection regulations such as the General Data Protection Regulation (GDPR) [95, 96].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
What the sources say
- Not checked yetcites [95] Voigt, P., Von dem Bussche, A.: The eu general data protection regulation (gdpr). A Practical Guide, 1st Ed., Cham: Springer International Publishing 10, 3152676 (2017) source
- Not checked yetcites [96] Khan, M.S.I., Rahman, A., Islam, S., Nasir, M.K., Band, S.S., Mosavi, A.: Iot and wsn based effluent treatment plant monitoring system. EasyChair Preprint no. 5023 (EasyChair, 2021) source
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Due to raw data transfer, this centralized approach is inefficient in terms of communication latency and cannot achieve high network scalability.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Introduction
What the sources say
- Not checked yetcites [5] Rahman, A., Islam, M.J., Saikat Islam Khan, M., Kabir, S., Pritom, A.I., Razaul Karim, M.: Block-sdotcloud: Enhancing security of cloud storage through blockchain-based sdn in iot network. In: 2020 2nd International Conference on Sustainable Technologies for Industry 4.0 (STI), pp. 1–6 (2020). https://doi.org/10.1109/STI50764.2020.9350419 source
- Not checked yetcites [6] Islam, M.J., Rahman, A., Kabir, S., Khatun, A., Pritom, A., Chowdhury, M.: Sdot-nfv: A distributed sdn based security system with iot for smart city environments. GUB Journal of Science and Engineering 7, 27–35 (2021) https://doi.org/10.3329/gubjse.v7i0.54015. https://www.banglajol.info/index.php/GUBJSE/article/view/54015 source
- Not checked yetcites [7] Hossen, R., Whaiduzzaman, M., Uddin, M.N., Islam, M., Faruqui, N., Barros, A., Sookhak, M., Mahi, M., Nayeen, J., et al.: Bdps: An efficient spark-based big data processing scheme for cloud fog-iot orchestration. Information 12(12), 517 (2021) source
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“More specifically, researchers have attempted to use AI to boost scientific analysis and analyse potential remedies, hence improving the overall efficacy of the healthcare industry [16, 17].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Introduction
What the sources say
- Not checked yetcites [16] Yu, K.H., Beam, A.L., Kohane, I.S.: Artificial intelligence in healthcare. Nat. Biomed. Eng. 2(10), 719–731 (2018) source
- Not checked yetcites [17] Peng, Y., Zhang, Y., Wang, L.: Guest editorial: Artificial intelligence in biomedical engineering and informatics: An introduction and review. Artificial Intell. Med. 48(2–3), 71–73 (2010) source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Accumulation of system or model parameters creates a global model using a server [72].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
What the sources say
- Not checked yetcites [72] Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 10(2), 1–19 (2019) source
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This study is the only potential and informative survey that integrates all the considered technologies to the best of our knowledge.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Introduction > Major contributions and organization of the survey
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“FL that has been shown in Fig. 1, it allows for equipping ML models with decentralized data and at the same time protects user confidential data by design.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Despite these attempts, as far as we know, no established research provides complete overview of FL-based AI uses in the sphere of smart healthcare.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Introduction
hedged- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The most recent advancements in FL, for instance FL aware of resources, safe and reliable FL, the potential of privacy-boosted FL, stimulus-aware FL, and tailored FL has yet to be extensively investigated.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Introduction
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“AI and its applications in healthcare have recently made considerable strides [19].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Introduction
What the sources say
- Not checked yetcites [19] Arikumar, K., Prathiba, S.B., Alazab, M., Gadekallu, T.R., Pandya, S., Khan, J.M., Moorthy, R.S.: Fl-pmi: Federated learning-based person movement identification through wearable devices in smart healthcare systems. Sensors 22(4), 1377 (2022) source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The server in FL aggregates the local data into a global network through an iterative process [91].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
What the sources say
- Not checked yetcites [91] Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečnỳ, J., Mazzocchi, S., McMahan, H.B., et al.: Towards federated learning at scale: System design. arXiv preprint arXiv:1902.01046 (2019)
plaindirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In practice, the user does not need to share their private data using FL.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
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In the paper
“Furthermore, the available literature lacks a comprehensive taxonomy of FL’s use in new healthcare applications.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Introduction
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“A Reasonable Balance of Accuracy and Utility: FL is capable of providing a suitable balance between precision and usefulness, as well as privacy enhancement, as compared to traditional centralized learning.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, FL training keeps the model generalizability while sacrificing nominal accuracy.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
plaindirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In Federated Learning, the information supplied to the server by each participant resembles no original data, rather a trained sub-model.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The ever growing number of IoT resources together with relevant applications means the requirement of processing large amount of data [98, 99].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
What the sources say
- Not checked yetcites [98] Rahman, A., Nasir, M.K., Rahman, Z., Mosavi, A., Shahab, S., Minaei-Bidgoli, B.: Distblockbuilding: A distributed blockchain-based sdn-iot network for smart building management. IEEE Access 8, 140008–140018 (2020) source
- Not checked yetcites [99] Rahman, A., Islam, M.J., Rahman, Z., Reza, M.M., Anwar, A., Mahmud, M.P., Nasir, M.K., Noor, R.M.: Distb-condo: Distributed blockchain-based iot-sdn model for smart condominium. IEEE Access 8, 209594–209609 (2020) source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“As a result, FL saves a significant amount of network bandwidth and reduces the risk of network congestion in large healthcare networks.”
- Also stated in: § e-Healthcare system · plain
“As a result, FL saves a significant amount of network bandwidth and reduces the risk of network congestion in large healthcare networks.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
plaindirect- Asserted: passes — The authors put it forward as true.
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- Also stated in: § e-Healthcare system · plain
In the paper
“Simultaneously, Federated Learning permits asynchronous transmission, which reduces requirements for communication purposes [92].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
What the sources say
- Not checked yetcites [92] Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC conference on computer and communications security, pp. 1310–1321 (2015) source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As a result, risk of personal data being leaked to an external source is reduced, and privacy is ensured.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The framework in FL allows the users to access data without gaining personal information regarding other clients.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“FL is highly effective in areas where decision making is based on substantial data scattered over a wide range of training nodes at the same time addressing privacy and security concern [100].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
What the sources say
- Not checked yetcites [100] Shaheen, M., Farooq, M.S., Umer, T., Kim, B.S.: Applications of federated learning; taxonomy, challenges, and research trends. Electronics 11(4), 670 (2022) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“FL is a highly effective method to gather EHR data from hospitals to provide a knowledge-sharing platform without the need of sharing personal data, all while preserving the privacy [104, 105].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
What the sources say
- Not checked yetcites [104] Gruendner, J., Schwachhofer, T., Sippl, P., Wolf, N., Erpenbeck, M., Gulden, C., Kapsner, L.A., Zierk, J., Mate, S., Stürzl, M., et al.: Ketos: Clinical decision support and machine learning as a service-a training and deployment platform based on docker, omop-cdm, and fhir web services. PloS One 14(10), e0223010 (2019) source
- Not checked yetcites [105] Huang, L., Shea, A.L., Qian, H., Masurkar, A., Deng, H., Liu, D.: Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records. J. Biomed Informat. 99, 103291 (2019) source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Regardless, as a result of bandwidth issues, security and storage facility, raw data transmission to a centralized location becomes unrealistic.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“Federated Learning approach is a concept introduced by google to allow on-device learning and security of sensitive data [93].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
What the sources say
- Not checked yetcites [93] Konečnỳ, J., McMahan, H.B., Yu, F.X., Richtárik, P., Suresh, A.T., Bacon, D.: Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“All these devices mean an exponential amount of generated datasets with an intense opportunity to learn from intelligent systems.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Artificial intelligence concepts
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Smart devices with measuring sensors are on the rise [117].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Artificial intelligence concepts
What the sources say
- Not checked yetcites [117] Helbing, D., Frey, B.S., Gigerenzer, G., Hafen, E., Hagner, M., Hofstetter, Y., Van Den Hoven, J., Zicari, R.V., Zwitter, A.: Will democracy survive big data and artificial intelligence? In: Towards digital enlightenment, pp. 73–98. Springer (2019) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, because the primary criterion of FL is privacy protection, the aggregation model cannot be realized in practice.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: The primary criterion of FL is privacy protection.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The traditional AI models usually necessitates the users to share individual information to a master network for learning purposes. A key concern with such techniques is the privacy of users sensitive information.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“FL has the benefit of distributed data processing and improved privacy factors.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Electronic healthcare records from multiple data sources (several hospitals) require joint data access and uploading of data to a single database.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Model-centric, cross-device, horizontal, cross-silo, vertical, data-centric, reinforcement, and other types of Federated Learning exist.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Fundamentals of federated learning
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Sensors capture information about the surroundings and individual activity in this scenario, which is subsequently evaluated via cloud computing or edge intelligence [124].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Artificial intelligence concepts
What the sources say
- Not checked yetcites [124] Hasan, M., Rahman, A., Islam, M.J.: Distb-cvs: A distributed secure blockchain based online certificate verification system from bangladesh perspective. In: 2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT), pp. 460–465 (2020). https://doi.org/10.1109/ICAICT51780.2020.9333523 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Moreover, through a semantic graph-based AI technique, researchers may effectively accomplish the complex work of outlining the literature on a specific topic [128].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Artificial intelligence concepts
What the sources say
- Not checked yetcites [128] Plaza, L., Díaz, A., Gervás, P.: A semantic graph-based approach to biomedical summarisation. Artificial intell. Med. 53(1), 1–14 (2011) source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“As a result, they must be flexible enough to operate purely on the basis of matching a model’s input to its outputs rather than relying on the model’s intrinsic architecture. [132]”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Artificial intelligence concepts
What the sources say
- Not checked yetcites [132] Dieber, J., Kirrane, S.: Why model why? assessing the strengths and limitations of lime. arXiv preprint arXiv:2012.00093 (2020)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Although transparency as a feature does not guarantee thet a model will be easily explainable, the decisions made by these models are frequently transparent. [130]”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Artificial intelligence concepts
What the sources say
- Not checked yetcites [130] Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Transparent Model: K-nearest neighbors (kNN), decision trees, rule-based learning, Bayesian networks, and are examples of typical transparent models.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Artificial intelligence concepts
What the sources say
- Not checked yetcites [130] Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (xai). IEEE Access 6, 52138–52160 (2018) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“AI can gather information from several sources and perform a task, solve critical problems or make a decision without any human instructions [120].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Artificial intelligence concepts
What the sources say
- Not checked yetcites [120] Kaplan, A., Haenlein, M.: Siri, siri, in my hand: Who’s the fairest in the land? on the interpretations, illustrations, and implications of artificial intelligence. Business Horizons 62(1), 15–25 (2019) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The primary goal of health-related AI applications is to investigate correlations between treatment or preventative strategies and patient outcomes [119].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Artificial intelligence concepts
What the sources say
- Not checked yetcites [119] Coiera, E.: Guide to medical informatics, the internet and telemedicine. Chapman & Hall, London (1997)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“These models are not transparent, despite the fact that they frequently reach excellent accuracy. [131]”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Artificial intelligence concepts
What the sources say
- Not checked yetcites [131] Angelov, P.P., Soares, E.A., Jiang, R., Arnold, N.I., Atkinson, P.M.: Explainable artificial intelligence: an analytical review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 11(5), e1424 (2021)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Because of the growing relevance of this topic, NIST published Four Rules of XAI in August 2020, which describe the following key principles [133] that an AI must follow in order to be designated a XAI:”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Artificial intelligence concepts
What the sources say
- Not checked yetcites [133] Phillips, P.J., Hahn, C.A., Fontana, P.C., Broniatowski, D.A., Przybocki, M.A.: Four principles of explainable artificial intelligence. Gaithersburg, Maryland (2020)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Opaque Model: Random forest, neural networks, Support Vector Machine (SVMs), and other opaque models are common.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Artificial intelligence concepts
What the sources say
- Not checked yetcites [131] Angelov, P.P., Soares, E.A., Jiang, R., Arnold, N.I., Atkinson, P.M.: Explainable artificial intelligence: an analytical review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 11(5), e1424 (2021)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“AI can potentially replace humans by working in wide-scale activities in the realm of industry, retail, finance, and healthcare with considerable impacts on system performance and efficiency [121] that depicted in Fig. 5.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Artificial intelligence concepts
What the sources say
- Not checked yetcites [121] López-Robles, J.R., Otegi-Olaso, J.R., Gómez, I.P., Cobo, M.J.: 30 years of intelligence models in management and business: A bibliometric review. Int. J. Informat. Manag. 48, 22–38 (2019) source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With the increased level of data, a system has the potential to increase its accuracy.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Artificial intelligence concepts
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“AI offers untapped potential as a strong tool in biological research [127], in addition to acting as a eDoctor for illness diagnosis, management, and prognosis.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Artificial intelligence concepts
What the sources say
- Not checked yetcites [127] Handelman, G., Kok, H., Chandra, R., Razavi, A., Lee, M., Asadi, H.: ed octor: machine learning and the future of medicine. J. Int. Med. 284(6), 603–619 (2018) source
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“They consist of AI systems that are both accurate and understandable by humans [129].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Artificial intelligence concepts
What the sources say
- Not checked yetcites [129] Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining explanations: An overview of interpretability of machine learning. In: 2018 IEEE 5th International Conference on data science and advanced analytics (DSAA), pp. 80–89. IEEE (2018) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“For accuracy over 90%, ML can be categorize biological queries into 4 main kinds [126].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > Artificial intelligence concepts
What the sources say
- Not checked yetcites [126] Sarrouti, M., El Alaoui, S.O.: A machine learning-based method for question type classification in biomedical question answering. Method. Informat. Med. 56(03), 209–216 (2017) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The e-healthcare system may give general support on a larger scale, such as management assistance, healthcare service delivery, and so on, as well as specialized help, such as citizen health data [135].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > e-Healthcare system
What the sources say
- Not checked yetcites [135] Muhammad, G., Rahman, S.K.M.M., Alelaiwi, A., Alamri, A.: Smart health solution integrating iot and cloud: a case study of voice pathology monitoring. IEEE Commun. Magazine 55(1), 69–73 (2017) source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The provision of health care via the internet and technology is carried out by combining existing tools and assuring the quality of the services supplied [136].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > e-Healthcare system
What the sources say
- Not checked yetcites [136] Garrett, L., Chowdhury, A.M.R., Pablos-Méndez, A.: All for universal health coverage. The Lancet 374(9697), 1294–1299 (2009) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The implementation of AI in healthcare sectors [47, 146] have created an urge amongst researchers to pursue innovative methods to develop e-healthcare systems.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > e-Healthcare system
What the sources say
- Not checked yetcites [47] Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., Zha, Y., Liang, W., Wang, C., Wang, K., et al.: Clinically applicable ai system for accurate diagnosis, quantitative measurements, and prognosis of covid-19 pneumonia using computed tomography. Cell 181(6), 1423–1433 (2020) source
- Not checked yetcites [146] Esmaeilzadeh, P.: Use of ai-based tools for healthcare purposes: a survey study from consumers’ perspectives. BMC Med. Informat. Decision Making 20(1), 1–19 (2020) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“This approach limits the danger of personal user data being leaked to an external source, ensuring better level of client security [149].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > e-Healthcare system
What the sources say
- Not checked yetcites [149] Zhao, Y., Zhao, J., Jiang, L., Tan, R., Niyato, D., Li, Z., Lyu, L., Liu, Y.: Privacy-preserving blockchain-based federated learning for iot devices. IEEE Internet of Things J. 8(3), 1817–1829 (2020) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The IoT and its application for healthcare management services can be divided into three steps [142]: i) gain access to the location of a patient or healthcare staff ii) to identify and authenticate an individual, and iii) to automate data collection using sensory devices.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > e-Healthcare system
What the sources say
- Not checked yetcites [142] Tsiounia, K., Dimitrioglou, N., Kardaras, D., Barbounaki, S.: A process modelling and analytic hierarchy process approach to investigate the potential of the iot in health services. In: World Congress On Medical Physics and Biomedical Engineering 2018, pp. 381–386. Springer (2019) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“IoT and smart devices are used to transfer the health data of a patient to the cloud server to access health information of an individual [141].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > e-Healthcare system
What the sources say
- Not checked yetcites [141] Asghari, P., Rahmani, A.M., Javadi, H.H.S.: Internet of things applications: A systematic review. Comp. Net. 148, 241–261 (2019) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“The prognosis of a patient infected with COVID-19 related illness is limited with high mortality [144, 145].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > e-Healthcare system
What the sources say
- Not checked yetcites [144] Muhammad, G., Alqahtani, S., Alelaiwi, A.: Pandemic management for diseases similar to covid-19 using deep learning and 5g communications. IEEE Network 35(3), 21–26 (2021) source
- Not checked yetcites [145] Muhammad, G., Hossain, M.S.: Deep learning-based edge-centric covid-19 like pandemic screening and diagnosis system within b5g framework using blockchain. IEEE Network 35(28), 74–81 (2021) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“The complexities of COVID-19 coupled with scarce resources and rising healthcare cost has encouraged many to access remote health management services using computer or similar devices [137–140].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > e-Healthcare system
What the sources say
- Not checked yetcites [137] Rahman, A., Chakraborty, C., Anwar, A., Karim, M., Islam, M., Kundu, D., Rahman, Z., Band, S.S., et al.: Sdn–iot empowered intelligent framework for industry 4.0 applications during covid-19 pandemic. Cluster Computing pp. 1–18 (2021) source
- Not checked yetcites [138] Muhammad, G., Hossain, M.S.: Covid-19 and non-covid-19 classification using multi-layers fusion from lung ultrasound images. Information Fusion 72, 80–88 (2021) source
- Not checked yetcites [139] Hossain, M.J., Wadud, M.A.H., Rahman, A., Ferdous, J., Alam, M.S., Amir Ul Haque Bhuiyan, T.M., Mridha, M.F.: A secured patient’s online data monitoring through blockchain: An intelligent way to store lifetime medical records. In: 2021 International Conference on Science Contemporary Technologies (ICSCT), pp. 1–6 (2021). https://doi.org/10.1109/ICSCT53883.2021.9642537 source
- Not checked yetcites [140] Masud, M., Gaba, G.S., Alqahtani, S., Muhammad, G., Gupta, B., Kumar, P., Ghoneim, A.: A lightweight and robust secure key establishment protocol for internet of medical things in covid-19 patients care. IEEE Internet of Things J. 8(21), 15694–15703 (2021) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Among the many applications of FL-based healthcare models for prediction, similarity among different patients is one [164].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Taxonomies and motivations of integrating FL with AI in healthcare system > Integrating of FL with AI in healthcare
What the sources say
- Not checked yetcites [164] Bakopoulou, E., Tillman, B., Markopoulou, A.: A federated learning approach for mobile packet classification. arXiv preprint arXiv:1907.13113 (2019)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There are various applications in the healthcare sector where FL integration will increase the overall effectiveness of the process analysis:”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Taxonomies and motivations of integrating FL with AI in healthcare system > Integrating of FL with AI in healthcare
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In this situation, data is not transported from source to destination in order to merge or generate a dataset for analysis; rather, the local client trains their model with their data and connects with other clients via the server bypassing only the results.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Taxonomies and motivations of integrating FL with AI in healthcare system > Integrating of FL with AI in healthcare
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This integration will benefit both suppliers, such as the organization in charge of producing and supplying the necessary equipment to a hospital, and users of the application, such as the general public.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Taxonomies and motivations of integrating FL with AI in healthcare system > Integrating of FL with AI in healthcare
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“With sophisticated XAI, the reason for the diagnosis of a certain cancer is apparent and visible to the system’s user [169].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Taxonomies and motivations of integrating FL with AI in healthcare system > Integrating of FL with AI in healthcare
What the sources say
- Not checked yetcites [169] Muhammad, G., Hossain, M.S., Guizani, N.: Explainable ai and mass surveillance system-based healthcare framework to combat covid-19 like pandemics. IEEE Network 34(4), 126–132 (2020) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As the linking sectors are vast in this era, and data production is also vast, it is difficult to identify malicious users among the thousands of connected clients; therefore, by leveraging the benefits of FL, the clients’ data is preserved safely with their own model, and data leakage is prevented to avoid any malicious data modification in the system.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Taxonomies and motivations of integrating FL with AI in healthcare system > Integrating of FL with AI in healthcare
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: The linking sectors are vast and data production is vast, making it difficult to identify malicious users among thousands of connected clients.
In the paper
“Some industrialized nations that have been adopting health-related activities have consistently prioritized eHealth [150].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > FL, AI, and healthcare: state-of-the-art > e-Healthcare system
What the sources say
- Not checked yetcites [150] Zaman, S.B.: Importance of learning the public health leadership. Public Health of Indonesia 3(1), 1–3 (2017) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Medicine resistance or therapy against various diseases, as well as survival rates or explanations, can be examined with strong privacy towards sensitive data [165].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Taxonomies and motivations of integrating FL with AI in healthcare system > Integrating of FL with AI in healthcare
What the sources say
- Not checked yetcites [165] Liu, Y., James, J., Kang, J., Niyato, D., Zhang, S.: Privacy-preserving traffic flow prediction: A federated learning approach. IEEE Internet of Things J. 7(8), 7751–7763 (2020) source
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Federated Learning, a distributed collaborative AI model, is specifically appealing for intelligent healthcare since it allows different clients (for example, hospitals) to collaborate on AI training without the need to share local data.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Taxonomies and motivations of integrating FL with AI in healthcare system > Taxonomies of FL with AI in healthcare
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“In the world of healthcare, the word artificial intelligence plays a very important and critical function in interpreting the human thinking process through the computer employing many efficient AI-based algorithms to make the difficult examination easy for healthcare professionals [167].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Taxonomies and motivations of integrating FL with AI in healthcare system > Integrating of FL with AI in healthcare
What the sources say
- Not checked yetcites [167] Muhammad, G., Alhussein, M.: Convergence of artificial intelligence and internet of things in smart healthcare: A case study of voice pathology detection. IEEE Access 9, 89198–89209 (2021) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As a result of the integration of Federated Learning with healthcare data informatics, the analysis of extremely sensitive data has become significantly more efficient, this integration scenario shown in Fig. 4.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Taxonomies and motivations of integrating FL with AI in healthcare system > Integrating of FL with AI in healthcare
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As a result, the data remain secure at their origin, and the network, gains access to the server’s training results.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Taxonomies and motivations of integrating FL with AI in healthcare system > Integrating of FL with AI in healthcare
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Then, some attacks from the intruder can access the information sent from the hardware system by directly attacking at the decryption point or by weakening the authentication procedure such as Eavesdropping attack, reply attack, MITM attack, rouge attack, DoS attack, DDoS attack and these type of attack causes to hamper the network layer of the IoMT [200].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Addressing and defending challenges on FL with AI in healthcare system > Existing challenges in healthcare system
What the sources say
- Not checked yetcites [200] Papaioannou, M., Karageorgou, M., Mantas, G., Sucasas, V., Essop, I., Rodriguez, J., Lymberopoulos, D.: A survey on security threats and countermeasures in internet of medical things (iomt). Transactions on Emerging Telecommunications Technologies p. e4049 (2020) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Preference, side-channel attack [198], data duplication using the tag, physical hampering of the sensor devices, or sensor tracking are some of the attacks that may cause issues in the perception layer [199].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Addressing and defending challenges on FL with AI in healthcare system > Existing challenges in healthcare system
What the sources say
- Not checked yetcites [198] Shayshab Azad, K.M., Hossain, N., Islam, M.J., Rahman, A., Kabir, S.: Preventive determination and avoidance of ddos attack with sdn over the iot networks. In: 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI), pp. 1–6 (2021). https://doi.org/10.1109/ACMI53878.2021.9528133 source
- Not checked yetcites [199] Kumar, R., Tripathi, R.: Towards design and implementation of security and privacy framework for internet of medical things (iomt) by leveraging blockchain and ipfs technology. The Journal of Supercomputing pp. 1–40 (2021) source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“When a faulty SQL code is injected into the database, it can jeopardize the system’s vital information [201].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Addressing and defending challenges on FL with AI in healthcare system > Existing challenges in healthcare system
What the sources say
- Not checked yetcites [201] Aman, A.H.M., Hassan, W.H., Sameen, S., Attarbashi, Z.S., Alizadeh, M., Latiff, L.A.: Iomt amid covid-19 pandemic: Application, architecture, technology, and security. J. Net. Comput. Appl. 174, 102886 (2021) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“One of the most serious topics is security, and privacy [197].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Addressing and defending challenges on FL with AI in healthcare system > Existing challenges in healthcare system
What the sources say
- Not checked yetcites [197] Hatzivasilis, G., Soultatos, O., Ioannidis, S., Verikoukis, C., Demetriou, G., Tsatsoulis, C.: Review of security and privacy for the internet of medical things (iomt). In: 2019 15th international conference on distributed computing in sensor systems (DCOSS), pp. 457–464. IEEE (2019) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Another issue in IoT-based systems is heterogeneity. Because the sensors are from diverse vendors, the system’s anonymity isn’t always guaranteed.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Addressing and defending challenges on FL with AI in healthcare system > Existing challenges in healthcare system
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“In today’s healthcare systems, a huge number of inbound and outbound networks must be managed. As a result, there is a problem with supporting a large number of high-speed wireless systems without causing system speed degradation [206].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Addressing and defending challenges on FL with AI in healthcare system > Existing challenges in healthcare system
What the sources say
- Not checked yetcites [206] Muhammad, G., Hossain, M.S., Kumar, N.: Eeg-based pathology detection for home health monitoring. IEEE J. Selected Areas in Commun. 39(2), 603–610 (2021) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Again, due to FL’s properties, imbalanced and non-IID information can be handled appropriately, ensuring that the AI system’s performance is not limited and that a large device in multiple contexts may be trained with their local data and local model without difficulty [211].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Addressing and defending challenges on FL with AI in healthcare system > Solving the existing challenges using FL with AI in the healthcare system
What the sources say
- Not checked yetcites [211] Wang, X., Han, Y., Wang, C., Zhao, Q., Chen, X., Chen, M.: In-edge ai: Intelligentizing mobile edge computing, caching and communication by federated learning. IEEE Network 33(5), 156–165 (2019) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“In this case, FL devised a privacy-preserving approach that does not require the transfer of sensitive data from the source, instead of training local models with their own data and then sharing results with the global network [209].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Addressing and defending challenges on FL with AI in healthcare system > Solving the existing challenges using FL with AI in the healthcare system
What the sources say
- Not checked yetcites [209] Vaid, A., Jaladanki, S.K., Xu, J., Teng, S., Kumar, A., Lee, S., Somani, S., Paranjpe, I., De Freitas, J.K., Wanyan, T., et al.: Federated learning of electronic health records to improve mortality prediction in hospitalized patients with covid-19: Machine learning approach. JMIR medical informatics 9(1), e24207 (2021) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“With the growth in data traffic, the network’s bandwidth is once again challenged [207].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Addressing and defending challenges on FL with AI in healthcare system > Existing challenges in healthcare system
What the sources say
- Not checked yetcites [207] Hussain, F., Abbas, S.G., Shah, G.A., Pires, I.M., Fayyaz, U.U., Shahzad, F., Garcia, N.M., Zdravevski, E.: A framework for malicious traffic detection in iot healthcare environment. Sensors 21(9), 3025 (2021) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The transit of data or sensitive information causes security breaches, which, in turn, causes a confidentiality problem in an IoMT-based system [205].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Addressing and defending challenges on FL with AI in healthcare system > Existing challenges in healthcare system
What the sources say
- Not checked yetcites [205] Jeyavel, J., Parameswaran, T., Mannan, J.M., Hariharan, U.: Security vulnerabilities and intelligent solutions for iomt systems. In: Internet of Medical Things, pp. 175–194. Springer (2021) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“As a result, to preserve the security and privacy of the hospital system, FL provides a privacy-preserving method while also providing the benefits of machine learning models to solve numerous difficulties related to healthcare systems [193, 210].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Addressing and defending challenges on FL with AI in healthcare system > Solving the existing challenges using FL with AI in the healthcare system
What the sources say
- Not checked yetcites [193] Long, G., Shen, T., Tan, Y., Gerrard, L., Clarke, A., Jiang, J.: Federated learning for privacy-preserving open innovation future on digital health. In: Humanity Driven AI, pp. 113–133. Springer (2022) source
- Not checked yetcites [210] Pokhrel, S.R., Choi, J.: A decentralized federated learning approach for connected autonomous vehicles. In: 2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), pp. 1–6. IEEE (2020) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Existing healthcare solutions necessitate the relocation and merging of data from multiple nodes to provide a long-term solution. However, because the data being moved is very sensitive, this data migration creates a confidentiality concern [203].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Addressing and defending challenges on FL with AI in healthcare system > Existing challenges in healthcare system
What the sources say
- Not checked yetcites [203] Rahman, M., Jahankhani, H.: Security vulnerabilities in existing security mechanisms for iomt and potential solutions for mitigating cyber-attacks. In: Information Security Technologies for Controlling Pandemics, pp. 307–334. Springer (2021) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“On the other Blockchain is a provider of distributed ledger for any vulnerable components [241].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Open issues and future direction > Future directions
What the sources say
- Not checked yetcites [241] Gulati, P., Sharma, A., Bhasin, K., Azad, C.: Approaches of blockchain with ai: Challenges & future direction. In: Proceedings of the International Conference on Innovative Computing & Communications (ICICC) (2020) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“As a result, storing or transferring these data in a short period of time is both difficult and critical [239].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Open issues and future direction > Future directions
What the sources say
- Not checked yetcites [239] Zhang, K., Song, X., Zhang, C., Yu, S.: Challenges and future directions of secure federated learning: a survey. Frontiers of computer science 16(5), 1–8 (2022) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: IoT sensors have less memory.
- Rests on an assumption: IoT sensors generate a massive amount of data every nanosecond.
None of the sources cited for this claim have been checked yet.
In the paper
“However, it is an advanced machine learning method that needs large datasets [233, 236].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Open issues and future direction > Open issues
What the sources say
- Not checked yetcites [233] Islam, S., Sara, U., Kawsar, A., Rahman, A., Kundu, D., Dipta, D.D., Karim, A.R., Hasan, M.: Sgbba: An efficient method for prediction system in machine learning using imbalance dataset. International Journal of Advanced Computer Science and Applications 12(3) (2021) source
- Not checked yetcites [236] Hasan, M., Rahman, A., Karim, M.R., Khan, M.S.I., Islam, M.J.: Normalized approach to find optimal number of topics in latent dirichlet allocation (lda). In: Proceedings of International Conference on Trends in Computational and Cognitive Engineering, pp. 341–354. Springer (2021) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Combined, they produce a highly protected environment that can continue their services to the current and future users incredibly [242, 243].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Open issues and future direction > Future directions
What the sources say
- Not checked yetcites [242] Rajawat, A.S., Bedi, P., Goyal, S., Shaw, R.N., Ghosh, A., Aggarwal, S.: Ai and blockchain for healthcare data security in smart cities. In: AI and IoT for Smart City Applications, pp. 185–198. Springer (2022) source
- Not checked yetcites [243] Latif, S.A., Wen, F.B.X., Iwendi, C., Li-li, F.W., Mohsin, S.M., Han, Z., Band, S.S.: Ai-empowered, blockchain and sdn integrated security architecture for iot network of cyber physical systems. Computer Communications 181, 274–283 (2022) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: AI applications need a secured platform to continue their activities.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The deep learning model’s black-box character, on the other hand, hinders its explainability and practical application in medicine.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Addressing and defending challenges on FL with AI in healthcare system > Solving the existing challenges using FL with AI in the healthcare system
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Rests on an assumption: Deep learning algorithms are typically black-box models, so there is no acceptable explanation for a given prediction.
- Rests on an assumption: Explainability is vital in healthcare because a given prediction must be explained to persuade a clinical healthcare practitioner and a patient.
None of the sources cited for this claim have been checked yet.
In the paper
“In this regard, people are utilizing various technologies into the health care systems such as SDN, Blockchain, etc [234, 235].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Open issues and future direction > Open issues
What the sources say
- Not checked yetcites [234] Rahman, A., Sara, U., Kundu, D., Islam, S., Islam, M.J., Hasan, M., Rahman, Z., Nasir, M.K.: Distb-sdoindustry: Enhancing security in industry 4.0 services based on distributed blockchain through software defined networking-iot enabled architecture. International Journal of Advanced Computer Science and Applications 11(9) (2020) source
- Not checked yetcites [235] Rahman, A., Islam, M.J., Montieri, A., Nasir, M.K., Reza, M.M., Band, S.S., Pescape, A., Hasan, M., Sookhak, M., Mosavi, A.: Smartblock-sdn: An optimized blockchain-sdn framework for resource management in iot. IEEE Access 9, 28361–28376 (2021) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“In this regard, FL is a well-suited solution since the key demanding task, data transfer, is forbidden in this scenario, ensuring data privacy while maintaining the autonomy system’s efficiency [244].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Open issues and future direction > Future directions
What the sources say
- Not checked yetcites [244] Xianjia, Y., Queralta, J.P., Heikkonen, J., Westerlund, T.: Federated learning in robotic and autonomous systems. arXiv preprint arXiv:2104.10141 (2021) source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: Training an agent necessitates the collection of large amounts of data from many sources, making secure data collection vital.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although AI in a federated context can address the concerns described previously, deep learning has an explainability difficulty.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Addressing and defending challenges on FL with AI in healthcare system > Solving the existing challenges using FL with AI in the healthcare system
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Deep learning algorithms are typically black-box models, so there is no acceptable explanation for a given prediction.
None of the sources cited for this claim have been checked yet.
In the paper
“So, the security could be given utilizing SDN, Blockchain, NFV, etc [237, 238].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Open issues and future direction > Open issues
What the sources say
- Not checked yetcites [237] Rahman, A., Islam, M.J., Karim, M.R., Kundu, D., Kabir, S.: An intelligent vaccine distribution process in covid-19 pandemic through blockchain-sdn framework from bangladesh perspective. In: 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), pp. 1–4 (2021). https://doi.org/10.1109/ICECIT54077.2021.9641303 source
- Not checked yetcites [238] Rahman, A., Rahman, M., Kundu, D., Karim, M.R., Band, S.S., Sookhak, M.: Study on iot for sars-cov-2 with healthcare: present and future perspective. Mathematical Biosciences and Engineering 18(6), 9697–9726 (2021) source
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The benefit of utilizing FL in this scenario is that the data storing mechanism is avoided and distributed among multiple clients, and because the data is trained locally on each client, the security of individual data is ensured [240].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Open issues and future direction > Future directions
What the sources say
- Not checked yetcites [240] Ahmed, U., Srivastava, G., Lin, J.C.W.: Reliable customer analysis using federated learning and exploring deep-attention edge intelligence. Future Generation Computer Systems 127, 70–79 (2022) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Humans can understand and describe how an AI system reached a decision using Explainable Artificial Intelligence (XAI) [212].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Addressing and defending challenges on FL with AI in healthcare system > Solving the existing challenges using FL with AI in the healthcare system
What the sources say
- Not checked yetcites [212] Gunning, D., Aha, D.: Darpa’s explainable artificial intelligence (xai) program. AI magazine 40(2), 44–58 (2019) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“In healthcare, AI performs a vital and crucial role in understanding the human thinking process through the computer using various effective AI-based algorithms to make complex examinations easier for healthcare professionals [83].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Open issues and future direction > Future directions
What the sources say
- Not checked yetcites [83] Xu, J., Glicksberg, B.S., Su, C., Walker, P., Bian, J., Wang, F.: Federated learning for healthcare informatics. J. Healthcare Informat. Res. 5(1), 1–19 (2021) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“Thus the medical data through the blockchain and Federated Learning security is ensured, and an efficient model can be assured [232].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Open issues and future direction > Future directions
What the sources say
- Not checked yetcites [232] Chang, Y., Fang, C., Sun, W.: A blockchain-based federated learning method for smart healthcare. Computational Intelligence and Neuroscience 2021 (2021) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: Medical data is extremely sensitive, and any changes could result in a massive loss for the entire system.
None of the sources cited for this claim have been checked yet.
In the paper
“The initial parameters then passed through the blocks of the blockchain technology for secure transmission [246, 247].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Open issues and future direction > Future directions
What the sources say
- Not checked yetcites [246] Xu, L., Zhou, X., Tao, Y., Yu, X., Yu, M., Khan, F.: Af relaying secrecy performance prediction for 6g mobile communication networks in industry 5.0. IEEE Transactions on Industrial Informatics (2021) source
- Not checked yetcites [247] Chander, B., Pal, S., De, D., Buyya, R.: Artificial intelligence-based internet of things for industry 5.0. In: Artificial Intelligence-based Internet of Things Systems, pp. 3–45. Springer (2022) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“As a result, data privacy is also ensured, and there is no need to transmit sensitive user data from one location to another [245].”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Open issues and future direction > Future directions
What the sources say
- Not checked yetcites [245] Xu, W., Fang, W., Ding, Y., Zou, M., Xiong, N.: Accelerating federated learning for iot in big data analytics with pruning, quantization and selective updating. IEEE Access 9, 38457–38466 (2021) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The authors state this as an assumption.
The global model is after an assignment is completed, and the associated learning model is after data aggregation.
“Assume the global model is after an assignment is completed, and associated learning model is after data aggregation.”
The authors state this as an assumption.
There is a fiduciary physician-patient connection in eHealth.
“Assuming that there is a fiduciary physician-patient connection in eHealth, the ethical obligations for the treatment of patient data in general and EHRs in particular that arise from the fiduciary relationship in standard health care also apply in eHealth [143].”
The paper relies on this without support.
The primary criterion of FL is privacy protection.
“However, because the primary criterion of FL is privacy protection, the aggregation model cannot be realized in practice.”
The paper relies on this without support.
Healthcare data is dispersed, not in the same format, and susceptible in nature.
“However, the data on the healthcare sector is dispersed. They are not in the same format, and the data is susceptible in nature, as it includes information from the medical industry.”
The paper relies on this without support.
Insurance sector data cannot be transmitted from one sector to another.
“The most sensitive of them are insurance sector data, which cannot be transmitted from one sector to another.”
The paper relies on this without support.
Without data transfer, processing with traditional learning and training methodologies is impossible.
“Without data transfer, processing with traditional learning and training methodologies is impossible, resulting in a significant gap in data analysis.”
The paper relies on this without support.
The linking sectors are vast and data production is vast, making it difficult to identify malicious users among thousands of connected clients.
“As the linking sectors are vast in this era, and data production is also vast, it is difficult to identify malicious users among the thousands of connected clients; therefore, by leveraging the benefits of FL, the clients’ data is preserved safely with their own model, and data leakage is prevented to avoid any malicious data modification in the system.”
The paper relies on this without support.
Traditional or statistically based prediction for the medical sector is problematic with expanding amount of data because it involves human data collection.
“Traditional or statistically based prediction for the medical sector is problematic with the expanding amount of data because it involves human data collection.”
The paper relies on this without support.
There is a significant demand for automation throughout data gathering, processing, and result creation processes.
“There is a significant demand for automation throughout the data gathering, processing, and result creation processes.”
The paper relies on this without support.
A system that can receive information from multiple sources and perform a task, solve crucial problems, or make a decision without human intervention would be extremely valuable to the healthcare business.
“Again, a system that can receive information from multiple sources and perform a task, solve crucial problems, or make a decision without human intervention would be extremely valuable to the healthcare business.”
The paper relies on this without support.
Deep learning algorithms are typically black-box models, so there is no acceptable explanation for a given prediction.
“Because deep learning algorithms are typically black-box models, there is no acceptable explanation for a given prediction.”
The paper relies on this without support.
Explainability is vital in healthcare because a given prediction must be explained to persuade a clinical healthcare practitioner and a patient.
“Explainability is vital in healthcare since we need to explain why a given prediction for sample input is made to persuade a clinical healthcare practitioner and a patient.”
The paper relies on this without support.
Some deep-learning-based diagnosis jobs are even more accurate than human medical specialists.
“Some deep-learning-based diagnosis jobs are even more accurate than human medical specialists.”
The paper relies on this without support.
Artificial intelligence approaches such as deep learning with the integration of FL have recently played a revolutionary role in healthcare, particularly in terms of diagnosis and surgery.
“Artificial intelligence approaches such as deep learning with the integration of FL have recently played a revolutionary role in healthcare, particularly in terms of diagnosis and surgery.”
The paper relies on this without support.
In these fields, these strategies have been proven to be beneficial.
“In these fields, these strategies have been proven to be beneficial.”
The paper relies on this without support.
Health-related data is more susceptible to privacy and security risks than other data.
“The health-related data is susceptible compared to the others.”
The paper relies on this without support.
The performance obtained from health data is not satisfactory because the domain of the information is not the same and the data contains outliers.
“Apart from this, the performance obtained from the health data is not satisfactory because the domain of the information is not the same and also contains outliers into the data points.”
The paper relies on this without support.
Processing health data and training machine learning models is expensive and time-consuming.
“Processing the health data and training machine learning models is expensive and time-consuming.”
The paper relies on this without support.
The generalization power of current machine learning models is not satisfactory in the medical sector.
“The generalization power of the current machine learning models is not satisfactory when it comes to the medical sector.”
The paper relies on this without support.
IoT sensors have less memory.
“the sensors that are associated with the IoT-based mechanism have less memory”
The paper relies on this without support.
IoT sensors generate a massive amount of data every nanosecond.
“They do, however, generate a massive amount of data for every nanosecond.”
The paper relies on this without support.
AI applications need a secured platform to continue their activities.
“AI applications need a secured platform to continue its activities in the applications environments.”
The paper relies on this without support.
Training an agent necessitates the collection of large amounts of data from many sources, making secure data collection vital.
“However, because training an agent necessitates the collection of large amounts of data from many sources, it is vital to provide secure data collection for training.”
The paper relies on this without support.
Medical data is extremely sensitive, and any changes could result in a massive loss for the entire system.
“However, medical data is extremely sensitive, and any changes could result in a massive loss for the entire system.”
The paper relies on this without support.
There is a high demand for standardization throughout data collection, processing, and result generation processes in healthcare.
“There is a high demand for standardization throughout the data collection, processing, and result generation processes.”
The paper relies on this without support.
Healthcare is not limited to only AI and FL techniques.
“healthcare is not limited to only AI and FL techniques”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The decentralized nature of FL addresses some privacy concerns by maintaining data on local devices, but it is still sensitive to security risks such as data poisoning, model inversion, and adversarial attacks [37].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [37]
Experimental — accuracy not yet measuredIn the citing paper
“In parallel, Federated Learning (FL) allows for training AI models on decentralized devices or servers that hold local data samples, negating the need to transfer sensitive patient data and thus reducing centralization concerns [13].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [13]
In the citing paper
“These models are better equipped to perform well across different patient populations and healthcare settings, as they are not limited to data from a single source.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [13]
In the citing paper
“FL offers a solution to this by enabling the development of models that learn from a diverse and representative dataset.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [13]
In the citing paper
“DP provides a mathematical framework that guarantees that the outcome of an analysis is not significantly affected by the inclusion or exclusion of any individual's data.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [13]
In the citing paper
“This means that even if someone had access to the output of a model trained with DP, they would not be able to infer much about any individual's data [13].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [13]
In the citing paper
“Since each institution trains a local model on its own data, the global model benefits from a wide range of data sources, leading to more accurate and robust AI models.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [13]
In the citing paper
“Federated Learning holds significant promise for overcoming privacy and data integrity challenges in healthcare AI.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [13]
In the citing paper
“Additionally, the risk of malicious clients attempting to compromise the model or infer sensitive information requires robust security mechanisms [13].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [13]
In the citing paper
“Communication efficiency becomes a concern when dealing with many participants, as the exchange of model updates can be bandwidth-intensive.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [13]
In the citing paper
“As healthcare data are often siloed across various institutions, FL allows for connecting these fragmented data sources while maintaining privacy.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [13]
In the citing paper
“By leveraging data from multiple institutions, FL can help create more accurate and generalizable models that reflect the global patient population, thus improving the quality of care [13].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [13]
In the citing paper
“The statistical heterogeneity of data across different institutions can lead to skewed models if not correctly managed.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [13]
In the citing paper
“FL can facilitate the development of robust, accurate, and generalizable AI models by enabling collaborative model training across various institutions while safeguarding data privacy.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [13]
In the citing paper
“Explainable AI (XAI) plays a foundational role in making AI decisions and processes more comprehensible, thus enhancing transparency and fostering trust.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [13]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Open issues and future direction
“Holbl et al. [188] | A mathematical algorithm to display the features of controllable network and blockchain based FL parameters to record its effect on system performance | Application of FL in the realm of vehicle communication medium that is effective.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > References
“188. Hölbl M Kompara M Kamišalić A Nemec Zlatolas L A systematic review of the use of blockchain in healthcare Symmetry 2018 10 10 470”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Open issues and future direction
“Greenberg et al. [66] | Factors for Delivering Safe Post surgical and Critical Care in the Event of a Medical Emergency | The difficulties that healthcare facilities across the world have encountered are mostly the result of a lack of preparedness.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > References
“66. Greenberg, N., Docherty, M., Gnanapragasam, S., Wessely, S.: Managing mental health challenges faced by healthcare workers during covid-19 pandemic.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Addressing and defending challenges on FL with AI in healthcare system > Solving the existing challenges using FL with AI in the healthcare system
“| El-Sappagh et al. [215] | Alzheimer’s Disease Real dataset | Random Forest | 87.76 | 0.953 |”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Addressing and defending challenges on FL with AI in healthcare system > Solving the existing challenges using FL with AI in the healthcare system
“The accuracy ranges from [0, 1], with 1 representing properly predicting all positive and negative samples and 0 representing successfully predicting none of the positive or negative samples.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Open issues and future direction
“Pokhrel et al. [189] | As outlined in this survey paper, many cryptocurrencies studies are currently being investigated. | Scientists in academic and industrial have begun to investigate applications aimed toward healthcare use, based on the existing blockchain technology.”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Addressing and defending challenges on FL with AI in healthcare system
“Pokhrel et al. [189] | Vehicle Machine Learning, Federated Learning, Blockchain. | - Complex framework | Proposed a FL method based on blockchain algorithm for security-aware and effective communication in vehicles”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Open issues and future direction
“Lu et al. [191] | Suffer from moral harm or mental health issues. | The covid-19 outbreak is going to place healthcare professionals all across the world in an unprecedented scenario”
§ Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues > Addressing and defending challenges on FL with AI in healthcare system
“Lu et al. [191] | Data Sharing, Permissioned Blockchain, Federated Learning, Privacy-preserved Industrial IoT | - Data leakage | Proposed a blockchain based secured data sharing method for several users.”
Citations
Cites
1 retracted
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RetractedFederated Machine Learning: Concept and Applications
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Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
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A guide to deep learning in healthcare
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Artificial intelligence in healthcare: past, present and future
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The future of digital health with federated learning
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“Rieke et al. [25] 2020 Discussed threats and potential remedies by leveraging FL in the healthcare sector.”
BackgroundFederated Learning: Strategies for Improving Communication Efficiency
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International evaluation of an AI system for breast cancer screening
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Deep learning for healthcare: review, opportunities and challenges
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Research works printed but not linked: 13
- Chakrobartty, S., El-Gayar, O.: Explainable artificial intelligence in the medical domain: A systematic review (2021)
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Items 101–200 of 399
AFM-DViT: A framework for IoT-driven medical image analysis
Yang, J. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.10.118 · Cited by 13
Ullah, A., Fawad, F., Nadeem, A., Arif, M., Bashir, M. M., & Choi, W. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101658 · Cited by 14
The Impact of Federated Learning on AI-Enhanced Healthcare Delivery
Shahi, A., & Mittal, A. (2023) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-2639-8.ch005 · Cited by 11
PI-Fed: Continual Federated Learning With Parameter-Level Importance Aggregation
Yu, L., Ge, L., Wang, G., Yin, J., Chen, Q., Zhou, J., & He, L. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3440029 · Cited by 11
A Differentially Private Blockchain-Based Approach for Vertical Federated Learning
Tran, L., Chari, S., Khan, M. S. I., Zachariah, A., Patterson, S., & Seneviratne, O. W. (2024) · IEEE International Conference on Decentralized Applications and Infrastructures (DAPPS) · doi:10.1109/dapps61106.2024.00020 · Cited by 13
Self-citationPanda, S., Dubey, R., Jena, B., Pareek, V., Tsai, L.-W., & Saxena, S. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.112747 · Cited by 11
AI in food sciences and technology – beyond the algorithms
Hoffman, L. C., & Cozzolino, D. (2025) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2025.2520397 · Cited by 11
Shafik, W., Hidayatullah, A. F., Kalinaki, K., Gul, H., Zakari, R. Y., & Tufail, A. (2026) · Health and Technology · doi:10.1007/s12553-025-01051-w · Cited by 20
A Decentralized Secure Blockchain-based Privacy-Preserving Healthcare Clouds and Applications
Wadud, M. A. H., Rahman, A., Islam, M. J., Bhuiyan, T. M. A.-U.-H., Hossain, M. J., & Hossen, R. (2023) · International Conference on Electrical, Computer and Communication Engineering · doi:10.1109/ecce57851.2023.10101540 · Cited by 10
Self-citationIoT-based Patient Monitoring System through Online Cloud and ECG Sensor
Bhuiyan, T. M. A.-U.-H., Ahmed, S., Salahin, M., Pollab, M., Jui, J. N., Ahmmed, M. T., Rahman, A., & Wadud, M. A. H. (2023) · International Conference on Next-Generation Computing, IoT and Machine Learning (NCIM) · doi:10.1109/ncim59001.2023.10212454 · Cited by 10
Self-citationDAG-Based Swarm Learning Approach in Healthcare: A Survey
Gana, D., & Jamil, F. (2025) · IEEE Access · doi:10.1109/access.2025.3531216 · Cited by 12
Future of the Medical World: Collaborative Medical Imaging AI With Federated Learning
Rahmaniar, W., Deng, Z., Yang, Y., Jin, Z., & Suzuki, K. (2025) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2025.3539474 · Cited by 9
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Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification
Gaikwad, N. S., Heublein, L., Raichur, N. L., Feigl, T., Mutschler, C., & Ott, F. (2025) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms57970.2025.11073735 · Cited by 12
Healthcare and cutting-edge technology: Advancements, challenges, and future prospects
Singhal, V., Shyam, R., Singhal, S., Tiwari, A., & Mangal, D. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110861 · Cited by 13
A Comparison of Explainable AI Models on Numeric and Graph-Structured Data
Avinash, A., Harikumar, A., Nair, A., Pai, S. K., Surendran, S., & George, L. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.04.088 · Cited by 11
Examining the applicability of the Protection of Personal Information Act in AI-driven environments
Mbonye, V., Moodley, M., & Nyika, F. D. (2024) · South African journal of information management · doi:10.4102/sajim.v26i1.1808 · Cited by 11
AI for all: bridging data gaps in machine learning and health
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Self-citationImran, M., & Lee, Y. (2026) · IEEE Access · doi:10.1109/access.2026.3656368 · Cited by 11
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Next Sentence Prediction: The Impact of Preprocessing Techniques in Deep Learning
Yuyun, Y., Latief, A. D., Sampurno, T., Hazriani, Arisha, A. O., & Mushaf, M. (2023) · International Conference on Computer, Control, Informatics and its Applications · doi:10.1109/ic3ina60834.2023.10285805 · Cited by 8
Security Challenges in Storing and Exchanging Medical Information
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Self-citationCategorization of Dehydrated Food through Hybrid Deep Transfer Learning Techniques
Nobel, S. M. N., Wadud, M. A. H., Rahman, A., Kundu, D., Aishi, A. A., Sazzad, S., Rahman, M., Imran, M. A., Sifat, O. F., Sayduzzaman, M., et al. (2024) · Statistics Optimization & Information Computing · doi:10.19139/soic-2310-5070-1896 · Cited by 8
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Online Federated Deep Probabilistic Learning-Based Smart Healthcare on Multi-Cloud Systems
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Emerging Trends in AI-Powered IoT: Innovations, Key Challenges, and Future Directions
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Artificial intelligence methods and approaches to improve data quality in healthcare data
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IoT Authentication in Federated Learning: Methods, Challenges, and Future Directions
Badhib, A., Alshehri, S., & Cherif, A. (2025) · Sensors · doi:10.3390/s25247619 · Cited by 8
Revolutionizing endodontics: the impact and innovations of artificial intelligence
Huang, X., Xu, Z., Chen, H., Guo, X., Yang, X., & Zhao, Y. (2026) · BMC Oral Health · doi:10.1186/s12903-025-07632-5 · Cited by 13
AI in Healthcare Data Analytics Trends and Transformative Innovations
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Securing Healthcare AI: Applied Federal Learning
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An Extensive Evaluation of New Federated Learning Approaches for COVID-19 Identification
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Novel Architecture Integrating XAI with Blockchain and IoT Devices for Healthcare
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A Review of Secure IoT Based Smart Health Monitoring System using Blockchain Technique
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Harnessing machine learning for improved diagnosis, drug discovery, and patient care
Paul, J. K., Azmal, M., Talukder, O. F., Haque, A. S. N. B., Meem, M., & Ghosh, A. (2025) · Computational and Structural Biotechnology Reports · doi:10.1016/j.csbr.2025.100051 · Cited by 8
BCT-FLHD: A blockchain-enabled federated learning framework for healthcare 5.0 disease detection
Tiwari, K., & Kumar, S. (2025) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-025-01983-0 · Cited by 7
FICAD: federated learning-based clustering for IoT data aggregation
AzharShokoufeh, D., Derakhshanfard, N., & Ghaffari, A. (2025) · Cluster Computing · doi:10.1007/s10586-025-05579-4 · Cited by 5
Decentralized proximity-aware clustering for collective self-federated learning
Domini, D., Farabegoli, N., Aguzzi, G., Mirko Viroli, D., & Esterle, L. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101841 · Cited by 5
Hijry, H. (2025) · Sustainability · doi:10.3390/su18010348 · Cited by 9
Randomized attention and dual-path system for electrocardiogram identity recognition
Sun, L., Li, H., & Muhammad, G. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.107883 · Cited by 5
Self-citationA Smart Healthcare Application using Artificial Intelligence (AI) and Machine Learning (ML)
Umbare, R., Patil, R., Mukund, T., Rathod, A., & Rajurkar, P. (2024) · International Conference on Image Processing and Capsule Networks (ICIPCN) · doi:10.1109/icipcn63822.2024.00041 · Cited by 5
Retracted: Understanding the Strengths and Limitations of AI for Data Access Optimization
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RetractedEmpowering Smart Cities through Federated Learning An Overview
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Application of Wearables in Healthcare Management: Recent Trends and Futuristic Approach
Wagholikar, S., & Wagholikar, O. (2023) · OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON) · doi:10.1109/otcon56053.2023.10113968 · Cited by 6
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Recent Trends of Federated Learning for Smart Healthcare Systems
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Priority-Based Resource Allocation Scheme for Resources Usage in Mobile Edge Computing Platform
Sharif, Z., Jung, L. T., Ayaz, M., Yahya, M., & Pitafi, S. (2023) · Journal of Hunan University Natural Sciences · doi:10.55463/issn.1674-2974.50.1.7 · Cited by 5
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Addressing data quality decompensation in federated learning via dynamic client selection
Fei, Q., Rodríguez-Barroso, N., Luzón, M. V., Zhang, Z.-L., & Herrera, F. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.108138 · Cited by 5
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Federated Learning Approaches to Diverse Machine Learning Model: A Review
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A Comparative Analysis of Federated Learning and Privacy-Preserving Techniques in Healthcare AI
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A Novel Federated Learning Framework for Healthcare Applications Using Wearable Devices
Natarajan, R., Krishna, S., & Christodoss, P. R. (2025) · IEEE International Conference on AI in Cybersecurity (ICAIC) · doi:10.1109/icaic63015.2025.10848974 · Cited by 4
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Federated Learning for Early Cardiac Anomaly Prediction in Cross-Silo IoMT Environments
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Novel Knowledge Graph-Based Modeling for Vulnerability Detection in the Internet of Medical Things
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Legal implications of federated learning integration in digital healthcare systems
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Khudoykulov, S., N., R. R., Aarthi, S., & Ashirova, A. I. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-3947-4.ch010 · Cited by 3
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Research on Federated Learning's Contribution to Trustworthy and Responsible Artificial Intelligence
Huang, S., Liang, Y., Shen, F., & Gao, F. (2024) · International Symposium on Robotics, Artificial Intelligence and Information Engineering (RAIIE) · doi:10.1145/3689299.3689322 · Cited by 3
Enhancing Edge-based Cardiovascular Diagnosis through Federated Learning and IoT
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Federated Learning in Agents Based Cyber-Physical Systems
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Weighted FedCOM: a communication efficient approach to federated learning
Kaushal, V., & Sharma, S. (2025) · Evolving Systems · doi:10.1007/s12530-025-09659-z · Cited by 3
Privacy-Secure and Decentralized Biometric Authentication Models Using Federated Learning Frameworks
Mathivanan., P., & Mahalakshmi, K. (2024) · International Conference on System, Computation, Automation and Networking (ICSCAN) · doi:10.1109/icscan62807.2024.10894284 · Cited by 3
Unlocking the Potential of AI in Healthcare
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Srinivasan, K., Prasanna, S., Midha, R., & Mohan, S. G. (2023) · EMITTER International Journal of Engineering Technology · doi:10.24003/emitter.v11i1.742 · Cited by 3
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Self-citationFederated Learning for Secure Healthcare Image Analysis in the Cloud
Varshney, N., Madan, P., Shrivastava, A., Kumar, C. P., Rao, A. L. N., & Khan, A. K. (2023) · International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI) · doi:10.1109/icaiihi57871.2023.10489066 · Cited by 3
Federated Learning for Enhanced Performance on Decentralized Healthcare Datasets
Ali, F., Kulkarni, A. V., Hussain, S., & Mhatre, N. (2024) · IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) · doi:10.1109/iatmsi60426.2024.10503059 · Cited by 3
Explainable AI (EXAI) for Smart Healthcare Automation
Shafik, W., & Lakshmi, D. (2024) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/979-8-3693-4439-2.ch012 · Cited by 3
Essentials for AI Research in Cardiology: Challenges and Mitigations
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Metadata
- Authors
- Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S.
- Affiliations
- University of Dhaka (BD)
- Mawlana Bhashani Science and Technology University (BD)
- King Saud University (SA)
- Aalto University (FI)
- National Yunlin University of Science and Technology (TW)
- Corresponding author
- Muhammad, Ghulam
- Band, Shahab S.
- ORCID
- Venue
- Cluster Computing
- Published
- 2022-08-17
- Volume
- 26
- Issue
- 4
- Pages
- 2271-2311
- ISSN
- 1386-7857 (ISSN-L)
- 1573-7543
- PMC
- PMC9385101
- OpenAlex
- W4292148537
- Cited by
- 399
- References
- 228
- Citations by year
- 2026: 93 · 2025: 149 · 2024: 99 · 2023: 53 · 2022: 5
- FWCI
- 25.17
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cluster Computing (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- King Saud University (RSP-2021/34, RSP-2021)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- federated learning, artificial intelligence in healthcare, explainable AI, smart healthcare, decentralized data processing, privacy preservation, healthcare security, healthcare management systems
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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